A novel threshold optimization of ML-CFAR detector in Weibull clutter using fuzzy-neural networks

  • Authors:

  • Amar Mezache;Faouzi Soltani

  • Affiliations:

  • Département d'Electronique, Faculté des Sciences de l'Ingénieur, Université de Constantine, Route d'Ain El Bey, Constantine 25000, Algeria;Département d'Electronique, Faculté des Sciences de l'Ingénieur, Université de Constantine, Route d'Ain El Bey, Constantine 25000, Algeria

  • Venue:
  • Signal Processing
  • Year:
  • 2007

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Abstract

This paper provides a novel and effective approach based on an adaptive neuro-fuzzy inference system for the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the maximum-likelihood CFAR (ML-CFAR) and the Censored ML-CFAR (CML-CFAR) detectors in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The theory of the FNN is presented and the genetic learning algorithm (GA) is applied for the training of the FNN threshold estimator. The proposed FNN-ML-CFAR and FNN-CML-CFAR detectors proved to be efficient particularly in the case of spiky clutter. Experimental results showed the effectiveness of an adaptive neuro-fuzzy threshold estimator under different system conditions and it is also shown that the optimal FNN-ML-CFAR and FNN-CML-CFAR detectors can achieve better performances than the conventional ML-CFAR and CML-CFAR algorithms.